3DCodeVerse: Exploring 3D Worlds as Code
3DCodeVerse pairs a 2M executable-code dataset with a harness that beats Claude Design in tests.
3DCodeVerse is a research ecosystem for generating editable, verifiable 3D content as executable code. It includes 3DCodeVerse-2M, a curated corpus built from open-source data, converted representations, and agent-generated programs, plus a multi-agent DAG harness that uses execution, spatial, and visual checks to revise outputs without human feedback. The authors establish baselines with fine-tuned open models and analyze harness components. Fine-tuning substantially improves open-source 3D coding, and the harness, when used with Claude Opus 5.5, achieves a 90% pairwise human-evaluation win rate against Claude Design for 3D objects.
- 3DCodeVerse-2M contains executable 3D programs with assets and verification records.
- A multi-agent DAG harness checks execution, spatial measurements, and visuals.
- Fine-tuning on the dataset improves open models’ 3D coding ability.
- With Claude Opus 5.5, the harness won 90% of comparisons against Claude Design.
Full article206 words · extracted from arxiv.org · click to collapse
We introduce 3DCodeVerse, a research ecosystem that enables coding agents to create editable and verifiable 3D content at scale. 3DCodeVerse comprises large-scale, high-quality datasets of executable 3D code, a coding harness for spatial reasoning and automated verification, and practical guidance for 3D coding. First, we construct 3DCodeVerse-2M, a large-scale curated dataset of standalone 3D executable code. We build this corpus by collecting open-source data, converting specialized representations, and generating new programs with coding agents. Each program is packaged with the source files and associated assets required for execution in its declared environment, along with available descriptions and verification records. Second, we develop the 3DCodeVerse Harness, a multi-agent directed acyclic graph framework for generating and refining 3D content. It uses execution checks, spatial measurements, and visual feedback to automatically evaluate and revise generated programs without human feedback during generation. Third, we conduct systematic empirical studies of 3D coding, establishing baselines with fine-tuned open models and deriving practical guidance through component-level analyses of our harness. Extensive experiments demonstrate that fine-tuning on our dataset significantly improves the 3D coding capabilities of open-source models. Notably, our 3DCodeVerse harness achieves a 90% win rate against Claude Design (3D Object) in pairwise human evaluations, when both are utilized by Claude Opus 5.5.
Text extracted automatically; images, tables and formatting may be missing. Original: https://arxiv.org/abs/2610.22341